I was curious if a niche blog post of mine had been slurped up by #ChatGPT so I asked a leading question—what I discovered is much worse. So far, it has told me:
• use apt-get on Endless OS
• preview a Jekyll site locally by opening files w/a web browser (w/o building)
• install several non-existent #Flatpak “packages” & extensions
It feels exactly like chatting w/someone talking out of their ass but trying to sound authoritative. #LLMs need to learn to say, “I don’t know.”
@cassidy "#LLMs need to learn to say, 'I don’t know.'"
Doing that properly might require... something that isn't an LLM. I'd say the LLM generates something that (statistically) looks like an answer, because that's what its trained to do.
Actually modeling some understanding of truth and knowledge might be a different and more difficult task than modeling language.
Nice example of how important emphasis can be for language understanding. Depending on which word in the sentence below is emphasized, it completely changes its meaning.
For #LLMs (and for our #ise2024 lecture) this means that learning to understand language purely from written text is probably not an "easy" task....
Saying "LLMs will eventually do every job" is a bit like:
Seeing Wifi wireless data
Then predicting "Wireless" Power saws (no electrical cord or battery) are just around the corner
It's a misapplication of the tech. You need to understand how #LLMs work and extrapolate that capability. It's all text people. Summarizing, collating, template matching. All fair game. But stray outside of that box and things get much harder.
I just tried a few AI plugins for #figma and they were all bad. This domain might be a great test for #LLMs . I predict these failings are unlikely to be fixed any time soon:
Layout was poor
They can't create components
Laughably complex object hierarchies (everything was enclosed in a frame)
Of course things will improve, but I expect fixing these deep structural problems are a function of many new constraints, likely beyond what today's LLMs are actually capable of. @simon ?
@simon my point being there are limits as to what #LLMs can do:
Structural
There is no clear API to "genAI" components
Training
There is very little training data on how to create a clean Figma object structure
These may be solved, eventually, but they also are likely quite different from the chat based solution patterns offered today. My concern is that it's much harder than boosters believe.
#AI#GenerativeAI#LLMs#ParetoCurves: "Which is the most accurate AI system for generating code? Surprisingly, there isn’t currently a good way to answer questions like these.
Based on HumanEval, a widely used benchmark for code generation, the most accurate publicly available system is LDB (short for LLM debugger).1 But there’s a catch. The most accurate generative AI systems, including LDB, tend to be agents,2 which repeatedly invoke language models like GPT-4. That means they can be orders of magnitude more costly to run than the models themselves (which are already pretty costly). If we eke out a 2% accuracy improvement for 100x the cost, is that really better?
In this post, we argue that:
AI agent accuracy measurements that don’t control for cost aren’t useful.
Pareto curves can help visualize the accuracy-cost tradeoff.
Current state-of-the-art agent architectures are complex and costly but no more accurate than extremely simple baseline agents that cost 50x less in some cases.
Proxies for cost such as parameter count are misleading if the goal is to identify the best system for a given task. We should directly measure dollar costs instead.
Published agent evaluations are difficult to reproduce because of a lack of standardization and questionable, undocumented evaluation methods in some cases."
"The biggest question raised by a future populated by unexceptional A.I., however, is existential. Should we as a society be investing tens of billions of dollars, our precious electricity that could be used toward moving away from fossil fuels, and a generation of the brightest math and science minds on incremental improvements in mediocre email writing?" (From an NYT article. See original thread.)
Do you REALLY want to get a feel for how GPT-4o does what it does? Just complete this poem — by doing so, you’ll have performed a computation similar to the one it does when you feed it a text-plus-image prompt.
Just FYI, if you have older parents or other family members, set up some sort of shibboleth with them so they know what to ask you if you ever call them asking for something. These new generative models are going to be extremely convincing, and the idiots in charge of these companies think they can use guardrails to stop it being used inappropriately. They can't. #genAI#LLMs#chatgpt